How British Columbia school district superintendents manage data
Bibliographic record
Abstract
The British Columbia Ministry of Education has been collecting demographic and academic performance information on every student in grades Kindergarten-12 since 1992. The amount of data held by the Ministry and now available for use is considerable: between 50 and 500 data elements have been collected from each of more than 500,000 students annually for the last 15 years. Most school districts also collect additional data on the performance of their students. School District Superintendents, as senior educational leaders in each school district, play vital roles in connecting data use to student learning and achievement. However, the patterns and strategies they employ to manage data use are largely unknown: consequently, the central research question of this dissertation is how British Columbia Superintendents manage data use in their districts. The study uses a Grounded Theory method to pursue this question. Twenty-two British Columbia Superintendents participated in interviews that each lasted between one and two hours. The resulting transcripts were analysed intensively to determine the underlying patterns and core variables at play. Eventually the following theory emerged: British Columbia School Districts improve their capacity to create and use data-based knowledge by combining staff engagement with structural support in such a way that the school district advances along a trajectory of increased data use in a series of five developmental phases. The theory offers a model that enables assessment of how far a school district has come and what possibilities there may be for further development of data-based knowledge. The model also provides Superintendents with an understanding of the actions that are critical for continuous improvement in the capacity of the district to use data effectively. The study suggests that the British Columbia Ministry of Education should provide overall leadership to develop organizational intelligence in the education system by modelling data-based knowledge use, building trust, working with districts to supply the necessary technology and data, and supporting processes that turn data into knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".